Dayo Salam

Customer

iFood, a leading online restaurant platform, partnered with me to leverage data science and enhance their direct marketing campaigns.

Challenge

iFood conducted a pilot campaign with 2,240 randomly chosen customers, contacting them via phone about the gadget. The campaign resulted in a net loss (-3.046MU) despite a 15% success rate. Their goal is to maximize profitability for their upcoming sixth campaign, promoting a new gadget to their existing customer base.

Solution

To maximize profitability and customer understanding for iFood's upcoming gadget campaign, I proposed a data science-driven solution encompassing several key steps:

• Deep Dive Data Exploration
  • Uncovered hidden patterns and relationships.
  • Identifed factors influencing gadget purchase decisions.
  • Gained a comprehensive understanding of ideal customers.
• Customer Segmentation for Targeted Marketing
  • Developed data-driven customer segments based on behavior.
  • Tailored future campaigns and messaging for each segment.
• Predictive Modeling for Profit Optimization
  • Built a classification model predicting customer response.
  • Identifed high-potential customers for campaign targeting.
  • Considered model explainability for future development.

Results

The classification model empowered iFood to significantly optimize their direct marketing campaigns. By targeting high-potential customers with the predictive model, iFood might have significantly increased the conversion rate (percentage of customers who buy the gadget) compared to the pilot campaign. Additionally, targeting the right audience with laser focus would likely reduce wasted marketing spend on customers less likely to be interested in the gadget, potentially leading to cost savings. This enabled them identified specific factors influencing purchase decisions. These insights then translated into actionable recommendations for iFood's marketing team, such as tailoring messaging or promotions to resonate better with different customer segments.